README update

README.md

@@ -20,19 +20,18 @@ It supports multiple optimizers, such as Adam or SGD, all the most common activa
 
 ## Usage
 
-Make sure your compiler supports C++17. Shkyera Grad is a header-only library, so the only thing you need to do is to include it in your project.
+Check out oour [Get Started Guide](https://fszewczyk.github.io/shkyera-grad/md_docs_tutorials_GetStarted.html) to learn the basics of _Shkyera Engine_.
 
-```cpp
-#include "include/ShkyeraGrad.hpp"
-```
+## Showcase
 
-Check out the [examples](examples/README.md) for a quick start on Shkyera Grad. In the meantime, here's a neural network that learns the XOR function.
+Here's a small example showcasing a feed-forward network learning the XOR function. Check out the `examples/` folder for more examples.
 
 ```cpp
-#include "include/ShkyeraGrad.hpp"
+#include "shkyera-grad/include/ShkyeraGrad.hpp"
 
 int main() {
     using namespace shkyera;
+    using T = Type::float32;
 
     std::vector<Vec32> xs;
     std::vector<Vec32> ys;
@@ -41,31 +40,39 @@ int main() {
     xs.push_back(Vec32::of({0, 0})); ys.push_back(Vec32::of({0}));
     xs.push_back(Vec32::of({1, 0})); ys.push_back(Vec32::of({1}));
     xs.push_back(Vec32::of({0, 1})); ys.push_back(Vec32::of({1}));
-    xs.push_back(Vec32::of({0, 0})); ys.push_back(Vec32::of({0}));
+    xs.push_back(Vec32::of({1, 1})); ys.push_back(Vec32::of({0}));
 
-    auto mlp = SequentialBuilder<Type::float32>::begin()
-                .add(Layer32::create(2, 15, Activation::relu<Type::float32>))
-                .add(Layer32::create(15, 5, Activation::relu<Type::float32>))
-                .add(Layer32::create(5, 1, Activation::sigmoid<Type::float32>))
-                .build();
+    auto network = SequentialBuilder<Type::float32>::begin()
+                    .add(Linear32::create(2, 15))
+                    .add(ReLU32::create())
+                    .add(Linear32::create(15, 5))
+                    .add(ReLU32::create())
+                    .add(Linear32::create(5, 1))
+                    .add(Sigmoid32::create())
+                    .build();
 
-    Optimizer32 optimizer = Optimizer<Type::float32>(mlp->parameters(), 0.1);
-    Loss::Function32 lossFunction = Loss::MSE<Type::float32>;
+    auto optimizer = Adam32(network->parameters(), 0.05);
+    auto lossFunction = Loss::MSE<T>;
 
-    // ------ TRAINING THE NETWORK ------- //
-    for (size_t epoch = 0; epoch < 100; epoch++) {
+    for (size_t epoch = 0; epoch < 100; epoch++) { // We train for 100 epochs
         auto epochLoss = Val32::create(0);
 
-        optimizer.reset();
-        for (size_t sample = 0; sample < xs.size(); ++sample) {
-            Vec32 pred = mlp->forward(xs[sample]);
-            auto loss = lossFunction(pred, ys[sample]);
+        optimizer.reset();                                      // Reset the gradients
+        for (size_t sample = 0; sample < xs.size(); ++sample) { // We go through each sample
+            Vec32 pred = network->forward(xs[sample]);          // We get some prediction
+            auto loss = lossFunction(pred, ys[sample]);         // And calculate its error
 
-            epochLoss = epochLoss + loss;
+            epochLoss = epochLoss + loss; // Store the loss for feedback
         }
-        optimizer.step();
+        optimizer.step(); // Update the parameters
+
+        auto averageLoss = epochLoss / Val32::create(xs.size());
+        std::cout << "Epoch: " << epoch + 1 << " Loss: " << averageLoss->getValue() << std::endl;
+    }
 
-        std::cout << "Epoch: " << epoch + 1 << " Loss: " << epochLoss->getValue() << std::endl;
+    for (size_t sample = 0; sample < xs.size(); ++sample) { // Go through each example
+        Vec32 pred = network->forward(xs[sample]);          // Predict result
+        std::cout << xs[sample] << " -> " << pred[0] << "\t| True: " << ys[sample][0] << std::endl;
     }
 }
 ```